Behavior of the Resources in the Growth of Social Network

  Results of plagiarism analysis from 2017-12-27 23:53 UTC 8.1%

  [doi 10.1109%2Ficeei.2015.7352551] M ahyuddin, K; Nasution, M .; Elveny, M arischa; Syah, Rahmad; Noah.pdf Date: 2017-12-27 15:19 UTC

  

  

  

  

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Behavior of the Resources in the Growth of Social

Network Mahyuddin K. M. Nasution

  y

  i

  is a set of transaction, and M

  i

  i = 1,…,n are subsets of attributes, or M i be subsets of Z. The implication X = Y with two possible values T = TRUE or F = FALSE as an association rule if X Y , are subsets of Z and X Y

  ŀ = ø. For representing some of attributes in search space

  ȍ, we denote a keyword as t x , an actor name as seed is t a , other actor names are t b , but in a document there are also attributes like t t is title, t

  

  event, t

  as publication year, etc. Supposing that the imptation of co-occurrence in a query as q = “t a AND t

  1 , z 2 , …, z | | Z

  x

  

   b is a collection of documents containing actor names t b

  . Then the transaction be M [0] i

  = {q b , j

  }, i = 1,…,n, j = 1,…,m for n seeds, or M i+1

  = {{q b , j

  },t y

  }, thus we have Q = D b

  } is a set of attributes literal, and each M

  In line with superficial methods, we have developed an approach using association rules to enhance the methods for extracting the social network of specialized web pages [12]. In this case, we were selecting some social actors as the seeds in order to enable declaring their names properly. It is used to reduce bias. For that purpose, we define the association rule as follows: Definition 1. Let Z z = {

  [0] Centre of Information System

  [6] Automatic extracting the social network is an relatively approach which is formed through modal relations [ 1], that

  Universitas Sumatera Utara Padang Bulan 20155 USU Medan Indonesia mahyuddin@usu. ac.id, nasutionmahyu@gmail. com

  [0] Marischa Elveny and Rahmad Syah

  Information Technology Departement, Fasilkom-TI Universitas Sumatera Utara Padang Bulan 20155 USU Medan Indonesia marischaelveny@gmail. com, rahmadsyah45@gmail. com

  [3] Shahrul Azman Noah

  Knowledge Technology Research Group, Faculty of Information Science & Technology Uiti Kebangsaan Malaysia Bangi 43600 UKM Selangor Malaysia samn@ftsm. ukm.my

  Abstract—Social network can be extracted from different sources of information, but the resources was growing dynamically require a flexible approach. Each social network has the resources, but the relationship between resources and information sources requires explanation. This paper is aimed to address the behavior of the resource in the growth of social networks by using the associ ation rule s and statistical calculations to explain the evolutionary mechanisms. There is a strong effect on the growth of the resources of social networks and totally behavior of resources has positive effect.

  Keywords—Superficial method; independence; multiple regression; association rule; timeline; total effect.

  I. I NTRODUCTION

  depends heavily on dycally the Web as information source [2]. In discrete mathematic literature, the social

  II. R EVIEW AND A PPROACH The use of Web is steadily gaining ground in extracting of social networks [1], but dealing with everything that can be changed dynamically in Web it needs a flexible approach [8]. There is most flexible method for extracting social network automatically from Web, that is the superficial method, but the method is less trustworthy [9]. Therefore, we use the evolutionary mechanism [10] for extracting social network and based on it we do the designing and choosing the rules [1].

  [2] network extraction formally has considered as a Certain product, it could be represented as a n n × matrix M of vertices v i in V as a set of actors, i = 1,…,n, and for generating their relations e j in E as a set of edges, j = 1,…,m, whereby e j = m ik in M is 1 for e j in E, if two actors v i and v k are adjacent, 0 otherwise [ hile the Web contained enormous amount of

  informationocial actors and clues about relations among them: We can always find new actors and add vertices to the

  [6] resultant network, but also occasionally we cannot find old actors whereby their representation on vertices we cannot eliminate it. In other word, we may create the new connection

  between actors or disconnect the original relation between t4,5].

  We considered that a social network as resources, i .

  e.

  [6] actor/vertex, relation/ edge, web/document, or connection/path ,

  [6] but no information about the relations of them as resources for explaining the dynamism of social network [ 6,7]. Therefore,

  this paper is aimed at addressing the dynamism of social network based on resources. In this case, the evolutionary mechanisms, a guide to express an approach.

  The 5th International Conference on Electrical Engineering and Informatics 2015 August 10-11, 2015, Bali, Indonesia 978-1-4673-7319-7/15/$31. 00 ©2015 IEEE

  j l =1,…, u ij

  E u (

  ) greater than one, see Fig. 1. Therefore, extracting social networks from the Web is

  [1] not only involve a number of documents/Web pages, but it also involves a dependence between the actors, relationships, and documents, whereby dependence test performed using chi square Ȥ

  2 to the contingency table with k rows and l columns, and by both k and l the degree of freedom [ 15] defines as

  follows Fig. 1. Timeline of social network based on a seed. df = (k - 1)(l - 1),

  ( 1) if the cells of table contain u

  ij

  , i = 1,…,k and j =1,…,l, then the expected value E u for all ij are

  ij

  )

  ) = ((Ȉ

  i k =1,…, u ij

  )(Ȉ

  j l =1,…, u ij

  ))/(Ȉ

  i k =1,…,

  Ȉ

  = (Ȉ(f

  2

  ) (2) and we have Ȥ

  is a subset of ȍ. This is a proof of the following theorem.

  , ( 3) where f o = u ij is the frequency of observed data and f e = E u ( ij

  as implication, where q in Q is a subset of ȍ, b j in D

  b

  is a subset of ȍ, t

  y

  in D

  b

  Theorem 1. If any query represents a co-occurrence, then the association rule is applicable in the search space ȍ.

  . A keyword has been used for retrieving information appropriately as expected, if the mentioned information exists in the search space [14]. Thus a keyword and a seed together to remove impact of ambiguity and bias that comes from search space. In this case, a seed is as bait for fishing all documents related to the seed. While a keyword is to rank all document according to suitability to information needed. A lot of Web pages have connection between any seed and others actors, where co- occurrence exist in the page, because of an event and a record in the document. Therefore, if a title represented a document, there are one or more names as authors of documents or Web pages, then presence of new actor names and their relationships can be sorted by ascending according to the published year of the document. Each seed would be established the relationship for the first time with the other actor, and the next relationship with more other different actors. The growth of social actors (vertices) and the relationships (edges) between a seed and other actors caused by increasing the number of document on Web, and indirectly it has described the dynamics of social networks.

  Corollary 1. If there are an actor in the search space and the actor as a seed, then via association rule there are one or more other actors in the search space.

  In real world, each people connected with other people. The Web is representation of the real world [13]. Therefore, in the Web page is possible two or more actors exist (co- occurrence). Is not every Web page created by the author as an actor is to discuss about other actors. Specially, in

  ȍ, one or more Web pages contain the tables as presentation of the online database. If a seed exits in one of Web pages and the seed is a part of query, then the Web page with a unique URL is possible has other actor names. For example, let t

  a

  = “Mahyuddin K. M. Nasution” and t

  x

  = “DBLP”, we have a query q = “Mahyuddin K. M. Nasution” AND “DBLP”, and q submitted to the search engine likes Google, we obtain a Web page has a table contains other names [12]. Generally, the search engine got one or more snippets about q and by rank, from the top to the bottom consist of snippets sequentially according to their compatibility to t a and t x

  2

  • – f
    • Ȉ

Definition 2. Let SN is a social network with n vertices. SN is a star network if d v ( ) = n-1 for one vertex only in SN or d v ( ) =1

  , we get an effect total tr as follows tr = ȕ

  2

  Ȉx

  1 x 2 .

  By dividing this equation by Ȉx

  1

  2

  1

  1

  1

  ȕ 2 .

  ( 5) In the same way, for y against x

  1 , x 2 and x

  3

  we have

  2

  Ȉx

  On the one hand, the timeline of network growth based on a seed (as an actor) show the history of his/hers social activities such as the relationship between the authors or the research collaboration in the academic social network. In this case, an actor is a centre in a star network and the network will grow with the emergence of other actors, where other actors might becomertex with degree d v ( i

  1

  o

  e

  for another.

  In the social network, the degree of a vertex d v ( ) is the number of other vertices to v they are connected. Or d v ( ) = k, v i in V i , = 1,…,n where k = n-1.

  )/f

  e

  ) is the frequency of expected data. On the other hand, a social network by relying to the seed and year variables will be accompanied with a timeline of growth. A timeline can be used to show a snapshot of evolutionary mechanism in order to predict the extent to which the growth of a social network, a list of events in chronological order, but this is to understand event and trends for a particular subject. We use the multiple-regression as an evolutionary mechanism for predicting a growth of social network, i.e. y to be regressed against two or more factors, and the relationship in multiple-regression [16] is y = ȕ

  i=1,…,k ȕ k x k .

  ( 4) In general, there is effect of y to x i , for instance i = 1,2, we have the multiple-regression of y against x

  1

  and x

  2 :

  Ȉx

  1 y = ȕ

  • ȕ

  • ȕ
  • ȕ

  • … + ȕ

  24

  4

  8

  9

  14

  15

  17

  20

  29

  1

  35

  40

  43

  51

  74

  85 109 121 139 168 243 299 451

  2

  3

  64

  3

  5

  2

  2

  3

  3

  3

  3

  4

  6

  50

  8

  15

  18

  23

  28

  32

  41

  45

  2

  5

  2

  EGRESSIONS Model x 1 = seed x

  27

  30

  48

  63

  71

  84 161 178 220 243 298 362 676 811 1297

  TABLE III. ǺETA OF M ULTOPLE

  2 = paper x 3 = vertex y

  20

  I II

  III 5.7799 0.6580

  y = -3.1476 + 0.6580x

  1

  2

  ( 8) Meanwhile, with the involvement of all the resources we acquired third model, y = -60.8712 – 0.0411x

  1

  2

  23

  13

  7

  41

  11

  11

  16

  18

  22

  24

  34

  46

  13

  55

  88

  97 121 138 159 188 284 332 496

  1

  1

  2

  3

  6

  2

  1

  3 (9)

  1) papers with the title, one or more authors, publication year and their affiliation, 2) vertices: the seeds and the new actor, and 3) edges: the relationship between actors.

  3 mean the indirect effect.

  Finally, we proposed an approach to extract social network as base for revealing the dynamism of social networks. An approach as follows

  1) make a query for representing co-occurrence of an actor name and a keyword, 2) submit a query to any search engine, 3) make sure the actor name and the keyword contained are exactly same such as in the query, 4) access the listed URL for extracting and then getting information.

  Furthermore, we will examine the dependence between all resources of social network and determine the behavior of dynamism based on predictive models.

  III. E

  XPERIMENTS

  By using the above approach and by involving 69 seeds grouped into 8 samples, we extract the social network, obtained information about

  We declare attributes of resources as follows: NoS as Number of Seeds, NoP as Number of Papers, NoNV as Number of New Vertices, and NoE as Number of Edges, see Table 1. Based on publication year documents we obtain an increase in the number of each factor in resources, see Table II.

  2

  If the sample contained in the Table I were calculated by using equation (2) such that the total expected through the equation (3) is greater than or same 0, then obtained Ȥ

  2

  = 458.5268. While based on equation (1) for k = 8, = 4 so that l df = 21 we have Ȥ

  2 0.05 = 11.6. If the proposed null hypothesis

  H lternative hypothesis H

  1

  as follows: H e resources of social network are not independent .

  ȕ

  ȕ

  H 1 : The resources of social network are independent.

  2

  tr = ȕ

  1

  1

  ȕ

  2

  1

  ȕ

  ȕ

  1

  3 , (6)

  where ȕ

  1

  means the direct effect, and ȕ

  1

  ȕ

  2

  ,…, ȕ

  

  [0]

  1

  24 120 112

  6

  11 115

  28

  84

  25

  43

  31

  44 124

  11

  40

  65

  45

  48 133 422 146 430

  86 164 105 169 413 TABLE II. T S N G

  IMELINE OF OCIAL ETWORK ROWTH [7] Year NoS NoP NoNV NoE 1980

  1986 1988 1990 1991 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

  1

  7

  7

  Then H rejected because Ȥ

  based on (4) is y = -3.7639 + 5.7799x

  2

  Ȥ

  2 0.05 . In other words, the

  emergence of a new vertex is associated with old vertex (a seed), the number of papers to be also dependent on the activities of the actor as a seed, and the number of edges are associated with the papers and the new vertices in social networks. Thus, there is a form of dependency between resources: actor/vertex, relation/edge, and Web/document.

  In context of the evolutionary mechanisms, the prediction models with resources take a position as conduit of information about dynamism of social network. The multiple- regressions are one of the methods to determine causal relationships between factors as resources of social network. Based on data in Table II we obtain an increase in the number of each factor in resource and then we conduct calculation.

  First model, therefore, involves papers as dependent variable y and seed as independent variable x

  1

  1 .

  11

  ( 7) Second model involves the relationship between authors, papers, and seeds. Vertex is a dependent variable which is determined by two other factors as independent variables: seed and paper,

  TABLE I. S G B S AMPLE ROUPS ASED ON EEDS Group NoS NoP NoNV NoE

  I II

  III

  IV V

  VIII

  8

  8

  • R
  • 0.0411 1.0177 12.0181 -0.6152 = papers = vertices = edges
    • 1.0177x
    • 12.0181x
      • – 8.1516x
      Briefly, three equations (7), (8) and (9) are summarized in Table III.

  Fig. 2. An effect network for 4 resources of social network

  IEEE International Symposium on [20] a World of Wireless, Mobile and Multimedia Networks (WoWMoM): 1- 6, 2011. [4] Marco Brambilla and Alessandro Bozzon, "Web data management through crowdsourcing upon social networks", IEEE/ACM International

  [16] G. Robins, P. Pattison, and S. Wasserman, " Logit models and logistic [6] regressions for social networks ",

  [15] Shusaku Tsumoto and Hirano, "Contingency table and granularity" , Annual Meeting [21] of the North American Fuzzy Information Processing Society (NAFIPS ), 665-669, 2007.

  [14] Mahyuddin K. M. Nasution, " New method for extracting keyword for [0] the social actor " , Intelligent Information and Databa se Systems (ACIIDS), LNCS Vol. 8397, Heidelberg, Springer, 83-92, 2014.

  " , Springer, [0] Heidelberg, Berlin, 2007.

  [13] P. Mika, " Social Networks and the Semantic Web

  Proceeding of 2011 International Conference [0] on Semantic Technology and Information Retrieval (STAIRS'11)), Putrajaya, Malaysia, IEEE, 64-69, 20

  [10] L. Jintao, R. Licheng, Y. Yinpu, Y., and X. Guangdong, "Research on the evolutionary mechanism of social network based on competitive selection", IEEE International Conference on Computational Aspects of Social Networks, 624-628, 2010. [11] S. Phelps, P. McBurney, P., and S. Parsons, "Evolutionary mechanism design: a review", Auton Agent Multi-Agent Syst 21, 237-264, 2010. [12] Mahyuddin K. M. Nasution and Shahrul Azman Noah, " Extraction of [0] academic social network from online database " , In rul Azman Mohd Noah et al. (eds. ),

  [9] Mahyuddin K . M . N asution, S . A . S . S aad, " Social network extraction: Superficial method and information retrieval ", In [0] [22] Proceeding of International Conference on Informatics for Developme (ICID'11), c2-110-c2-115, 2011.

  International Conference on Information Retrieval & Knowledg Management (CAMP12 ), Putrajaya, Malaysia, 2012.

  [7] Stella Heras, Katie Atkinson, Vicente Botti, Floriana Grasso, Vicente Juli'an, and Peter McBurney, "Research opportunities for argumentation in social networks", Artif. Intell. Rev. 39, 39-62, . [8] Mahyuddin K. M. Nasution and ShaAzman Noah , " Information [0] retrieval model : A social network extraction perspective ", IEEE [0] [0]

  

[19]

" Incremental K-clique clustering in dynamic social networks " , Artif. [19] Intell. Rev. 38, 129-147, 2012.

  pringer-Verlag Berlin Heidelberg, 115-125, 2007. [6] eng Duan, Yuhua Li, Ruixuan Li , and Zhengding Lu,

  Conference on Advances in Social Network Analysis and Mining: 1123- 1127, 2012. [5] Steve Hanneke and Eric P. Xing. 2007. "Discrete temporal models of social networks", In E. M. Airoldi et al. (Eds.): ICML 2006 Ws, LNCS

  [3] Marco Conti, Andrea Passarella, and Fabio Pezzoni, "A model for the generation of social network graphs" ,

  Table III contains beta values for determining behavior of resources of social network that is to measure effect of each resource to growth of social network. As showed by Fig. 2. From beta values there is direct effect of seeds to increase number o f e dges, b ut s trong e ffect o f s eed i s t o g rowth o f papers and vertices, while the papers have strongest effect for growing edges. The last, vertices have strong effect for existing edges. It is detailed as follows,

  Dissertation, Universiti Kebangsaan Malaysia (in Malay), 2013.

  References [1] Mahyuddin K. M. Nasution and S. A. M. Noah, " Superficial method for [0] extracting social networacademic using Web snippets" , In Yu, J. et al., (eds.). Rough Set and Knowledge Technology, LNAI 6401 , [0] Heidelberg, Springer, 483-490, 2010. [2] Mahyuddin K. M. Nasution (Mahyuddin), "Kaedah dangkal bagi pengekstrakan rangkaian sosial akademik dari Web", Ph.D.

  IV. C ONCLUSION In this study we have presented an analysis for finding behavior of resources of social network. The resources of social networks – actor/vertex, relation/edge, Web/document, or connection/path – have different behavior toward growth of social networks based on social network extracting by using association rule. In total, the effects of resources positively affect the growth of vertices and edges in a social network based on predictions models. The future work will involve the extraction of a social network to describe the research collaboration.

  And the effect total is tr = 16.1092. So even though the resources have different effects on the growth of social networks, but overall all the resources effect the dynamic of social networks.

  3 = (5.7799)(1.0177)(- 9.1516) = -47.9494.

  and x

  2

  4) indirect effect via x

  3 ) = (0.6580)(-8.1516) = -5.3638.

  ) = (5.7799)(12.0181) = 69.4634. 3) indirect effect via the vertices (x

  2

  ) toward y = -0.0411, 2) indirect effect via the papers (x

  1

  1) direct effect of the seeds (x

  Psychometrika, 64, 371-394, 1999 . [6]